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Abstract: —J. Blunden and T. Boyer In 2023, La Niña conditions that generally prevailed in the eastern Pacific Ocean from mid-2020 into early 2023 gave way to a strong El Niño by October. Atmospheric concentrations of Earth’s major greenhouse gases—carbon dioxide, methane, and nitrous oxide—all increased to record-high levels. The annual global average carbon dioxide concentration in the atmosphere rose to 419.3±0.1 ppm, which is 50% greater than the pre-industrial level. The growth from 2022 to 2023 was 2.8 ppm, the fourth highest in the record since the 1960s. The combined short-term effects of El Niño and the long-term effects of increasing levels of heat-trapping gases in the atmosphere contributed to new records for many essential climate variables reported here. The annual global temperature across land and oceans was the highest in records dating as far back as 1850, with the last seven months (June–December) having each been record warm. Over land, the globally averaged temperature was also record high. Dozens of countries reported record or near-record warmth for the year, including China and continental Europe as a whole (warmest on record), India and Russia (second warmest), and Canada (third warmest). Intense and widespread heatwaves were reported around the world. In Vietnam, an all-time national maximum temperature record of 44.2°C was observed at Tuong Duong on 7 May, surpassing the previous record of 43.4°C at Huong Khe on 20 April 2019. In Brazil, the air temperature reached 44.8°C in Araçuaí in Minas Gerais on 20 November, potentially a new national record and 12.8°C above normal. The effect of rising temperatures was apparent in the cryosphere, where snow cover extent by June 2023 was the smallest in the 56-year record for North America and seventh smallest for the Northern Hemisphere overall. Heatwaves contributed to the greatest average mass balance loss for Alpine glaciers around the world since the start of the record in 1970. Due to rapid volume loss beginning in 2021, St. Anna Glacier in Switzerland and Ice Worm Glacier in the United States disappeared completely. In August, as a direct result of glacial thinning over the past 20 years, a glacial lake on a tributary of the Mendenhall Glacier in Alaska burst through its ice dam and caused unprecedented flooding on Mendenhall River near Juneau. Across the Arctic, the annual surface air temperature was the fourth highest in the 124-year record, and summer (July–September) was record warm. Smaller-than-normal snow cover extent in May and June contributed to the third-highest average peak tundra greenness in the 24-year record. In September, Arctic minimum sea ice extent was the fifth smallest in the 45-year satellite record. The 17 lowest September extents have all occurred in the last 17 years. In Antarctica, temperatures for much of the year were up to 6°C above average over the Weddell Sea and along coastal Dronning Maud Land. The Antarctic Peninsula also experienced well-above-average temperatures during the 2022/23 melt season, which contributed to its fourth consecutive summer of above-average surface melt. On 21 February, Antarctic sea ice extent and sea ice area both reached all-time lows, surpassing records set just a year earlier. Over the course of the year, new daily record-low sea ice extents were set on 278 days. In some instances, these daily records were set by a large margin, for example, the extent on 6 July was 1.8 million km2 lower than the previous record low for that day. Across the global oceans, the annual sea surface temperature was the highest in the 170-year record, far surpassing the previous record of 2016 by 0.13°C. Daily and monthly records were set from March onward, including an historic-high daily global mean sea surface temperature of 18.99°C recorded on 22 August. Approximately 94% of the ocean surface experienced at least one marine heatwave in 2023, while 27% experienced at least one cold spell. Globally averaged ocean heat content from the surface to 2000-m depth was record high in 2023, increasing at a rate equivalent to ∼0.7 Watts per square meter of energy applied over Earth’s surface. Global mean sea level was also record high for the 12th consecutive year, reaching 101.4 mm above the 1993 average when satellite measurements began, an increase of 8.1±1.5 mm over 2022 and the third highest year-over-year increase in the record. A total of 82 named tropical storms were observed during the Northern and Southern Hemispheres’ storm seasons, below the 1991–2020 average of 87. Hurricane Otis became the strongest landfalling hurricane on record for the west coast of Mexico at 140 kt (72 m s−1), causing at least 52 fatalities and $12–16 billion U.S. dollars in damage. Freddy became the world’s longest-lived tropical cyclones on record, developing into a tropical cyclone on 6 February and finally dissipating on 12 March. Freddy crossed the full width of the Indian Ocean and made one landfall in Madagascar and two in Mozambique. In the Mediterranean Sea—outside of traditional tropical cyclone basins—heavy rains and flooding from Storm Daniel killed more than 4300 people and left more than 8000 missing in Libya. The record-warm temperatures in 2023 created conditions that helped intensify the hydrological cycle. Measurements of total-column water vapor in the atmosphere were the highest on record, while the fraction of cloud area in the sky was the lowest since records began in 1980. The annual global mean precipitation total over land surfaces for 2023 was among the lowest since 1979, but global one-day maximum totals were close to average, indicating an increase in rainfall intensity. In July, record-high areas of land across the globe (7.9%) experienced extreme drought, breaking the previous record of 6.2% in July 2022. Overall, 29.7% of land experienced moderate or worse categories of drought during the year, also a record. Mexico reported its driest (and hottest) year since the start of its record in 1950. In alignment with hot and prolonged dry conditions, Canada experienced its worst national wildfire season on record. Approximately 15 million hectares burned across the country, which was more than double the previous record from 1989. Smoke from the fires were transported far into the United States and even to western European countries. August to October 2023 was the driest three-month period in Australia in the 104-year record. Millions of hectares of bushfires burned for weeks in the Northern Territory. In South America, extreme drought developed in the latter half of the year through the Amazon basin. By the end of October, the Rio Negro at Manaus, a major tributary of the Amazon River, fell to its lowest water level since records began in 1902. The transition from La Niña to El Niño helped bring relief to the prolonged drought conditions in equatorial eastern Africa. However, El Niño along with positive Indian Ocean dipole conditions also contributed to excessive rainfall that resulted in devastating floods over southeastern Ethiopia, Somalia, and Kenya during October to December that displaced around 1.5 million people. On 5 September, the town of Zagora, Greece, broke a national record for highest daily rainfall (754 mm in 21 hours, after which the station ceased reporting) due to Storm Daniel; this one-day accumulation was close to Zagora’s normal annual total.

Diabetes mellitus (DM) is a chronic endocrine disorder that affects more than 20 million people in the United States. DM-related complications affect multiple organ systems and are a significant cause of morbidity and mortality among people with DM. Of the numerous acute and chronic complications, atherosclerosis due to diabetic dyslipidemia is a condition that can lead to many life-threatening diseases, such as stroke, coronary artery disease, and myocardial infarction. The nuclear erythroid 2-related factor 2 (Nrf2) signaling pathway is an emerging antioxidative pathway and a promising target for the treatment of DM and its complications. This review aims to explore the Nrf2 pathways role in combating diabetic dyslipidemia. We will explore risk factors for diabetic dyslipidemia at a cellular level and aim to elucidate how the Nrf2 pathway becomes a potential therapeutic target for DM-related atherosclerosis.

Diabetes is a chronic disease that induces many comorbidities, including cardiovascular disease, nephropathy, and liver damage. Many mechanisms have been suggested as to how diabetes leads to these comorbidities, of which increased oxidative stress in diabetic patients has been strongly implicated. Limited knowledge of antioxidative antidiabetic drugs and substances that can address diabetic comorbidities through the nuclear factor erythroid 2-related factor 2 (Nrf2) pathway calls for detailed investigation. This review will describe how diabetes increases oxidative stress, the general impact of that oxidative stress, and how oxidative stress primarily contributes to diabetic comorbidities. It will also address how treatments for diabetes, especially focusing on their effects on the Nrf2 antioxidative pathway, have been shown to similarly affect the Nrf2 pathway of the heart, kidney, and liver systems. This review demonstrates that the Nrf2 pathway is a common pathogenic component of diabetes and its associated comorbidities, potentially identifying this pathway as a target to guide future treatments.

Over the last two decades, an increasing number of cognitive scientists have turned to mechanistic explanatory frameworks in their efforts to describe and explain cognitive phenomena. Most mechanistic frameworks conceive of cognitive systems as composed of functionally-individuated components whose functions are narrowly defined by their ranges of possible inputs and outputs, as well as their relations to other components within the phenomenon-producing mechanism. In this paper, I argue that this modular view of cognitive mechanisms as linear systems is not applicable to biological cognitive systems, and offer an alternative characterization using the methodology of Dynamical Systems Theory.

The scourge of type-1 diabetes (T1D) is the morbidity and mortality it and its complications cause at a younger age. This propels the constant search for better diagnostic, treatment, and management strategies, with the ultimate quest being a cure for T1D. Recently, the therapeutic potential of exosomes has generated a lot of interest. Among the characteristics of exosomes of particular interest are (a) their regenerative capacity, which depends on their origin, and (b) their content, which determines the cell communication and crosstalk they influence. Other functional capacities, including paracrine and endocrine homeostatic regulation, pathogenic response ability resulting in insulin secretory defects or β-cell death under normal metabolic conditions, immunomodulation, and promotion of regeneration, have also garnered significant interest. Exosome specificity makes them suitable as biomarkers or predictors, and their mobility and content lend credence to drug delivery and therapeutic suitability. This review aims to highlight the functional capacities of exosomes and their established as well as novel contributions at various pathways in the onset and progression of T1D. The pathogenesis of T1D involves a complex crosstalk between insulin-secreting pancreatic β-cells and immune cells, which is partially mediated by exosomes. We also examine the potential implications for type 2 diabetes (T2D), as the link in T2D has guided T1D exploration. The collective landscape presented is expected to help identify how a deeper understanding of exosomes (and their cargo) can provide a framework for actionable solutions to prevent, halt, or change the very course of T1D and its complications.

Abstract: Convergence science is an intrepid form of interdisciplinarity defined by the US National Research Council as ‘the coming together of insights and approaches from originally distinct fields’ to strategically address grand challenges. Despite its increasing relevance to science policy and institutional design, there is still no practical framework for measuring convergence. We address this gap by developing a measure of disciplinary distance based upon disciplinary boundaries delineated by hierarchical ontologies. We apply this approach using two widely used ontologies—the Classification of Instructional Programs and the Medical Subject Headings—each comprised of thousands of entities that facilitate classifying two distinct research dimensions, respectively. The social dimension codifies the disciplinary pedigree of individual scholars, connoting core expertise associated with traditional modes of mono-disciplinary graduate education. The conceptual dimension codifies the knowledge, methods, and equipment fundamental to a given target problem, which together may exceed the researchers’ core expertise. Considered in tandem, this decomposition facilitates measuring social-conceptual alignment and optimizing team assembly around domain-spanning problems—a key aspect that eludes other approaches. We demonstrate the utility of this framework in a case study of the human brain science (HBS) ecosystem, a relevant convergence nexus that highlights several practical considerations for designing, evaluating, institutionalizing, and accelerating convergence. Econometric analysis of 655,386 publications derived from 9,121 distinct HBS scholars reveals a 11.4% article-level citation premium attributable to research featuring full topical convergence, and an additional 2.7% citation premium if the social (disciplinary) configuration of scholars is maximally aligned with the conceptual (topical) configuration of the research.

Species distribution models (SDMs) are becoming an important tool for marine conservation and management. Yet while there is an increasing diversity and volume of marine biodiversity data for training SDMs, little practical guidance is available on how to leverage distinct data types to build robust models. We explored the effect of different data types on the fit, performance and predictive ability of SDMs by comparing models trained with four data types for a heavily exploited pelagic fish, the blue shark (Prionace glauca), in the Northwest Atlantic: two fishery dependent (conventional mark-recapture tags, fisheries observer records) and two fishery independent (satellite-linked electronic tags, pop-up archival tags). We found that all four data types can result in robust models, but differences among spatial predictions highlighted the need to consider ecological realism in model selection and interpretation regardless of data type. Differences among models were primarily attributed to biases in how each data type, and the associated representation of absences, sampled the environment and summarized the resulting species distributions. Outputs from model ensembles and a model trained on all pooled data both proved effective for combining inferences across data types and provided more ecologically realistic predictions than individual models. Our results provide valuable guidance for practitioners developing SDMs. With increasing access to diverse data sources, future work should further develop truly integrative modeling approaches that can explicitly leverage the strengths of individual data types while statistically accounting for limitations, such as sampling biases.

The Northwest Atlantic Ocean and Gulf of Mexico are among the fastest warming ocean regions, a trend that is expected to continue through this century with far-reaching implications for marine ecosystems. We examine the distribution of 12 highly migratory top predator species using predictive models and project expected habitat changes using downscaled climate models. Our models predict widespread losses of suitable habitat for most species, concurrent with substantial northward displacement of core habitats >500 km. These changes include up to >70% loss of suitable habitat area for some commercially and ecologically important species. We also identify predicted hot spots of multi-species habitat loss focused offshore of the U.S. Southeast and Mid-Atlantic coasts. For several species, the predicted changes are already underway, which are likely to have substantial impacts on the efficacy of static regulatory frameworks used to manage highly migratory species. The ongoing and projected effects of climate change highlight the urgent need to adaptively and proactively manage dynamic marine ecosystems.

Stem cell therapy is an attractive treatment for diseases in companion animals that cannot be treated by conventional veterinary medicine practices. The unique properties of stem cells, particularly the ability to differentiate into specific cell types, makes them a focal point in regenerative medicine treatments. Stem cell transplantation, especially using mesenchymal stem cells, has been proposed as a means to treat a wide range of injuries and ailments, resulting in tissue regeneration or repair. This review aims to summarize the veterinary use of stem cells for treating age-related and joint diseases, which are common conditions in pets. While additional research is necessary and certain limitations exist, the potential of stem cell therapy for companion animals is immense.

Physiologic insulin secretion consists of an oscillating pattern of secretion followed by distinct trough periods that stimulate ligand and receptor activation. Apart from the large postprandial bolus release of insulin, β cells also secrete small amounts of insulin every 4-8 min independent of a meal. Insulin resistance is associated with a disruption in the normal cyclical pattern of insulin secretion. In the case of type-2 diabetes, β-cell mass is reduced due to apoptosis and β cells secrete insulin asynchronously. When ligand/receptors are constantly exposed to insulin, a negative feedback loop down regulates insulin receptor availability to insulin, creating a relative hyperinsulinemia. The relative excess of insulin leads to insulin resistance (IR) due to decreased receptor availability. Over time, progressive insulin resistance compromises carbohydrate metabolism, and may progress to type-2 diabetes (T2D). In this review, we discuss insulin resistance pathophysiology and the use of dynamic exogenous insulin administration in a manner consistent with more normal insulin secretion periodicity to reverse insulin resistance. Administration of insulin in such a physiologic manner appears to improve insulin sensitivity, lower HgbA1c, and, in some instances, has been associated with the reversal of end-organ damage that leads to complications of diabetes. This review outlines the rationale for how the physiologic secretion of insulin orchestrates glucose metabolism, and how mimicking this secretion profile may serve to improve glycemic control, reduce cellular inflammation, and potentially improve outcomes in patients with diabetes.